An exploration of on-road vehicle detection using hierarchical scaling schemes

This paper targets at detecting preceding vehicles in a wide range of distance. We propose an Adaboost-based approach combined with hierarchical image and sub-window scaling schemes. The relationship is investigated among object characteristics, image structures and image scales. A parameter set is developed to easily adjust overall performance, which benefits researchers to establish a vehicle detection system. It achieves 96.6% detection rate with 2.0% false alarm rate along proposed methodology. The benchmark of several learning-based vehicle detection approaches is also provided. The results show the outperformance of the proposed method.